Robust automatic segmentation of various anatomical structures in a medical image is a key enabler in improving clinical workflows. Here, the term segmentation refers to the identification of the anatomical structure in the medical image, e.g., by delineation of the boundaries of the anatomical structure, by labeling of the voxels enclosed by the boundaries, etc. Once such segmentation has been performed, it is possible to extract clinical parameters such as, in case of, e.g., a cardiac structure, ventricular mass, ejection fraction and wall thickness. When overlaid over, or otherwise applied to the medical image, the segmentation may also provide an annotation of the anatomical structure in the medical image.
It is known to segment an anatomical structure in a medical image manually. For example, a user may, using a graphical user interface, delineate a boundary of the anatomical structure in the medical image. Disadvantageously, such manual segmentation is a time-consuming and thereby cumbersome activity, and ultimately error prone.
It is also known to segment an anatomical structure in a medical image automatically using a model. Such type of segmentation is also referred to as model-based segmentation. The model may be defined by model data. The model data may define a geometry of the anatomical structure, e.g., in the form of a mesh of triangles or a (densely sampled) point cloud. In case of a mesh-based model, inter-patient and inter-disease-stage shape variability may be modeled by assigning an affine transformation to each part of the mesh. Affine transformations cover translation, rotation, scaling along different coordinate axes and shearing. Mesh regularity may be maintained by interpolation of the affine transformations at the transitions between different parts of the model. Such affine transformations are often used as a component in so-termed ‘deformable’ models.
The fitting of a model to the image data of the medical image may involve an adaptation technique, also termed ‘mesh adaptation’ in case of a mesh-based model. Such applying is therefore also referred to as ‘adapting’. The adaptation technique may optimize an energy function based on an external energy term which adapts the model to the image data and an internal energy term which maintains a rigidness of the model.
Models of the above described type, as well as other types, are known per se, as are various adaptation techniques for the applying of such models to a medical image.
For example, a publication titled “Automatic Model-based Segmentation of the Heart in CT Images” by O. Ecabert et al., IEEE Transactions on Medical Imaging 2008, 27(9), pp. 1189-1201, describes a model-based approach for the automatic segmentation of the heart from three-dimensional (3D) Computed Tomography (CT) images.